A model predictive control for a trickle bed reactor

Abstract

This research presents the implementation of a model predictive control (MPC) strategy to control a trickle bed reactor (TBR) in which catalytic hydrogenations of pyrolysis gasoline take place. As the success of MPC applications relies on the availability of models of the system to be controlled, dynamic distributed process models consisting of kinetic expressions for the gasoline hydrogenation, and mass and energy balances for the reactor have been developed. An optimization problem is formulated to determine the kinetic parameters minimizing an error between model prediction and plant data. The process models developed are used in the formulation of the MPC controller for controlling the temperature of the trickle bed reactor. The performance of the MPC scheme is demonstrated in cases of set point tracking and disturbance rejection. The simulation results have shown that the MPC provides a better control prformance compared with a conventional PID controller: In addition to applying the MPC technique to the trickle bed reactor, this research investigates the performance of the MPC in controlling batch and continuous chemical reactors. In the case of the batch reactor, the MPC is applied to improve an operation by on-line modifying an optimal temperature set point profile. In the case of the continuous reactor, the MPC is utilized to control a product concentration. Simulation results have demonstrated that the MPC control strategy is applicable to control as well as improve the efficiency of both reactors with great success.